Recursive self-improvement

Recursive self-improvement is an approach to Artificial General Intelligence that allows a system to make adjustments to its own functionality resulting in improved performance. The system could then feedback on itself with each cycle reaching ever higher levels of intelligence resulting in either a hard or soft AI takeoff. This is distinct from simple "self-improvement". The word "recursive" means here that the modification improves its ability to improve.

Recursively self-improving AI is considered to be the push behind the intelligence explosion. While any sufficiently intelligent AI will be able to improve itself, Seed AIs are specifically designed to use recursive self-improvement as their primary method of gaining intelligence. Architectures that had not been designed with this goal in mind, such as neural networks or large "hand-coded" projects like Cyc, would have a harder time self-improving.

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Self-improvement in humans

The human species has made an enormous amount of progress since evolving around fifty thousand years ago. This is because we can pass on knowledge and infrastructure from previous generations. This is a type of self-improvement, but it is not recursive. If we never learned to modify our own brains, then we would eventually reach the point where making new discoveries required more knowledge than could be gained in a human lifetime. All human progress to date has been limited by the hardware we are born with, which is the same hardware Homo sapiens were born with fifty thousand years ago. True recursive self-improvement will come when we discover how to modify or augment our own brains in order to be more intelligent. This would lead us to more quickly being able to discover how to become even more intelligent. Assuming the rate is fast enough, this initiates a positive feedback loop.